The PhD Student Council at the University of Skövde hosted a panel discussion on the topic of AI in research. Together with Peter Anderberg, Jörgen Hansson, Amos Ng, and Jane Synnergren, we discussed our views, informed by the perspectives from our different areas of research. We touched upon various aspects of the topic, and concluded with questions from the audience.
Many thanks to Sara Yousif Mohamed Mahmoud and Vipul Vijayan Nair for the invitation and organising a very enjoyable and interesting event!
You can watch the recording at Sara’s youtube channel.
The slides for my presentation at the International Symposium on Forecasting 2020 are available here.
Building on the work by Pangiotelis et al. (2020) we investigate the implications of the geometric interpretation of hierarchical forecasting further. We propose a new framework for generating hierarchical forecasts, which encompasses previous hierarchical methods while providing insights on their behaviour. A key takeaway is that it is possible to obtain more efficient solutions to the hierarchical forecasting problem. Nonetheless, even though these may be more efficient, the optimisation remains non-trivial. In this work, we identify a series of approximations that balance the efficiency gains with the optimisation complexity to provide superior hierarchical forecasts, as evidenced in our empirical evaluation.
We are inviting submissions to a special issue at the International Journal of Forecasting on the topic of “Innovations in Hierarchical Forecasting”. The special issue is guest edited by G. Athanasopoulos, R. J. Hyndman, A. Panagiotelis and myself and its submission deadline is on the 31st of August 2021.
Organisations make multiple decisions informed by forecasts both to plan and function efficiently. Such decisions may differ in scope, from operational, to tactical, to strategic; corresponding to different time scales from short-term to medium-term to long-term; and can have different foci, for example inventory control for a single product, for a single store, or across an entire supply chain. Organisations that face such challenges include businesses, not-for-profit organisations, and policymakers who address societal challenges.
Forecasts of quantities that adhere to some known constraints should be coherent; that is, the predicted values at disaggregate scales should add up to the aggregate forecast. For example, monthly predictions should sum up to annual predictions and similarly, regional predictions should add up to country-level predictions. This is an important qualifier for forecasts to support aligned decision making across different planning units and horizons.
This forecasting problem setting gives rise to hierarchical forecasting. Historically this has been addressed using Top-Down and Bottom-Up approaches, which have been shown to exhibit several limitations. In the past decade, the introduction of forecast reconciliation approaches has reinvigorated research into hierarchical forecasting. Recent work has looked at novel estimation techniques, expansion of the hierarchical framework to temporal and cross-temporal hierarchies, probabilistic hierarchical forecasting, alternative understandings of the problem through a geometric or optimisation lens, amongst many other contributions. Meanwhile, forecast reconciliation techniques have been applied to a number of novel domains including energy, macroeconomics, mortality, tourism, and intermittent demand.
Areas of interest include, but are not limited to:
New methodologies for hierarchical forecasting
High dimensional hierarchical forecasting (methods and applications)
An improved understanding of the relationship between forecast reconciliation and forecast combination
Probabilistic hierarchical forecasting
Temporal and cross-temporal hierarchies
Machine learning and AI approaches to hierarchical forecasting
Hierarchical forecasting with explanatory variables
Applications of hierarchical forecasting to new domains
We would like to see submissions from diverse backgrounds, reflecting the nature of forecasting in organisations. You can find additional information here.
Together with Devon Barrow and Sven Crone, we gave a talk at the recent OR 62 conference, moderated by Christina Phillips. The topic was: “The quest for greater forecasting accuracy: Perspectives from Statistics & Machine Learning”. I have worked with both Devon and Sven in the past years and the three of us share quite a few perspectives on what are the promising avenues for forecasting, but also have our diverging views, influenced by our research interests and interactions with the industry. The discussion reflects that, and I think that there are a few helpful points about the future of the various disciplines in forecasting. Of course, we dutifully avoid making too many forecasts about forecasting!
Last week we run the first workshop of the Forecasting Forum Scandinavia, hoping to start an ongoing discussion between academia and practice around forecasting and predictive analytics. The vision is for this to be the catalyst in:
providing innovative solutions to real business problems, at a rigorous scientific standard;
shorten the path to implementing innovative and impactful research to practice;
create consortia between and within industry and academia to facilitate ambitious research by sharing know-how, resources, and risk.
The topic of the workshop the use of information from the business and market environment to enhance forecasts. You can find slides and recordings of all the talks in the forum’s LinkedIn group.
My talk focused on the academic perspective, and a gave a non-technical overview of:
What are the elements of a “good” forecast? (I keep the quotation, as I did not touch upon loss functions and objectives.)
Limitations of extrapolative forecasting and some motivations for using external predictors. (You won’t get me saying causal models! We are still so far away from being able to claim causality!)
Potential variables to enhance your forecasts and relevant considerations.
You can find the slides here and a recording of the talk below.
A few words on the “we”. With David Fagersand, who is the CEO of Indicio Technologies, we share the view that there is a substantial gap in the interaction between academia and industry on forecasting and predictive analytics, at least in Sweden and neighbouring Scandinavia – although my experience is that this is a wider challenge (more on that below!). We both recognise that there is strength in putting different perspectives and objectives together, to keep some balance between academia and practice. I do not think it is contested that academia can be “too academic” at times, and practice “too practical” (see a previous opinion piece co-authored with Fotios Petropoulos here). Obviously, Indicio is a company and therefore for-profit. I nowadays work at a Skövde university in Sweden, that is a public university, which in line with my ethos for freely accessible knowledge and open-source. My personal view is that bringing these two sides together can only be beneficial! My view for the ideal evolution of initiative is to be less driven by individuals, and more by the interaction in the community. It would be great if organisations would openly speak about the challenges they face and provide the means to universities to help them solve them. It would also be great if more academics would get their hands dirty! And obviously outstanding if it is widely acknowledged that such an initiative to run requires both resources and speakers! So, a call for action for current and future members!
I will expand a bit on this. Over my academic career I had the luck to work with many great colleagues and some of the biggest companies internationally. Naturally, at every country the business culture and the academic attitude differs. It will come as no surprise than some foster impactful research and innovation more than others. I find Sweden to be a great place for this, with both companies and universities focusing more into how to get exciting work done, rather than how to split the pie – necessary, but let’s get the priorities right when you involve academia: we are not consultants (at that point!). I find that organisations (and that includes universities ironically!) often do not understand how resource intensive research has become. It needs time, very skilled people, computing resources, data and time. Did I mention time? More importantly, training new academics is critical, and that requires the investment by all industry, academia and state. Let me also add that the skill often does not come solely from academia. We are all smart people, so if we ask someone/a team to outsmart us all and solve a very difficult problem, at least let’s give then the resources!
I would not expect from academics to always get the economics right (unless they are economists? – of course we have the responsibility to get it right!), but if companies are into money making, they surely understand that there is no free lunch! We face great societal challenges, and we all need to play our part. Improving forecasts is not just fun (for academics), or impacting the bottom line (for companies), it is also important for a more sustainable society and environment in the large scheme of things, but also for meeting the needs of societies. The initiative is called Forecasting Forum Scandinavia, but I would so much like to see the name proving to be wrong and becoming an international community of people eager to solve problems, meet challenges, and contribute!
I am delighted to receive the news that my recent paper with George Athanasopoulos at the European Journal of Operational Research has been selected as the EJOR editor’s choice article for June 2020. My thanks to the editor and the reviewers for their help with their comments and recommendations in improving the paper and bringing it to its current form.
You can find the June 2020 selected papers here. The final online version of article will be available for free for the next three months.
I plan to write a post about the gist of the idea.
Devon Barrow, Nikolaos Kourentzes, Rickard Sandberg, and Jacek Niklewski, 2020. Expert Systems with Applications.
A major challenge in automating the production of a large number of forecasts, as often required in many business applications, is the need for robust and reliable predictions. Increased noise, outliers and structural changes in the series, all too common in practice, can severely affect the quality of forecasting. We investigate ways to increase the reliability of exponential smoothing forecasts, the most widely used family of forecasting models in business forecasting. We consider two alternative sets of approaches, one stemming from statistics and one from machine learning. To this end, we adapt M-estimators, boosting and inverse boosting to parameter estimation for exponential smoothing. We propose appropriate modifications that are necessary for time series forecasting while aiming to obtain scalable algorithms. We evaluate the various estimation methods using multiple real datasets and find that several approaches outperform the widely used maximum likelihood estimation. The novelty of this work lies in (1) demonstrating the usefulness of M-estimators, (2) and of inverse boosting, which outperforms standard boosting approaches, and (3) a comparative look at statistics versus machine learning inspired approaches.
Intermittent demand forecasting has been widely researched in the context of spare parts management. However, it is becoming increasingly relevant to many other areas, such as retailing, where at the very disaggregate level time series may be highly intermittent, but at more aggregate levels are likely to exhibit trends and seasonal patterns. The vast majority of intermittent demand forecasting methods are inappropriate for producing forecasts with such features. We propose using temporal hierarchies to produce forecasts that demonstrate these traits at the various aggregation levels, effectively informing the resulting intermittent forecasts of these patterns that are identifiable only at higher levels. We conduct an empirical evaluation on real data and demonstrate statistically significant gains for both point and quantile forecasts.
Inaccurate forecasts can be costly for company operations, in terms of stock-outs and lost sales, or over-stocking, while not meeting service level targets. The forecasting literature, often disjoint from the needs of the forecast users, has focused on providing optimal models in terms of likelihood and various accuracy metrics. However, there is evidence that this does not always lead to better inventory performance, as often the translation between forecast errors and inventory results is not linear. In this study, we consider an approach to parametrising forecasting models by directly considering appropriate inventory metrics and the current inventory policy. We propose a way to combine the competing multiple inventory objectives, i.e. meeting demand, while eliminating excessive stock, and use the resulting cost function to identify inventory optimal parameters for forecasting models. We evaluate the proposed parametrisation against established alternatives and demonstrate its performance on real data. Furthermore, we explore the connection between forecast accuracy and inventory performance and discuss the extent to which the former is an appropriate proxy of the latter.
Achieving high accuracy in energy consumption forecasting is critical for improving energy management and planning. However, this requires the selection of appropriate forecasting models, able to capture the individual characteristics of the series to be predicted, which is a task that involves a lot of uncertainty. When hierarchies of load from different sources are considered together, the uncertainty and complexity increase further. For example, when forecasting both at system and region level, not only the model selection problem is expanded to multiple time series, but we also require aggregation consistency of the forecasts across levels. Although hierarchical forecasting, such as the bottom-up, the top-down, and the optimal reconciliation methods, can address the aggregation consistency concerns, it does not resolve the model selection uncertainty. To address this issue, we rely on Multiple Temporal Aggregation (MTA), which has been shown to mitigate the model selection problem for low-frequency time series. We propose a modification of the Multiple Aggregation Prediction Algorithm, a special implementation of MTA, for high-frequency time series to better handle the undesirable effect of seasonality shrinkage that MTA implies and combine it with conventional cross-sectional hierarchical forecasting. The impact of incorporating temporal aggregation in hierarchical forecasting is empirically assessed using a real data set from five bank branches. We show that the proposed MTA approach, combined with the optimal reconciliation method, demonstrates superior accuracy, aggregation consistency, and reliable automatic forecasting.